Infrared spectroscopy is a relatively rapid, sensitive, and chemically specific technique for the detection of a large variety of chemicals. However, there are difficulties associated with implementing the technology in a continuous gas detection monitor system requiring a very low false alarm rate and high sensitivity.
Infrared spectroscopy is a well-known technique for chemical identification and quantification. The observed infrared signal I is linked to chemical concentration by Beer's Law equation I(ν)=Io (ν)e−ε(ν)cl, where Io is the background signal, ε is the absorption coefficient for the chemical of interest, c is the chemical concentration, and l is the path length over which the chemical is observed. I, Io, and ε are all functions of the light frequency ν. Concentration is determined by solving a concentration equation c=−log [I(ν)/Io(ν)]/ε(ν)l.
A critical variable in the concentration equation is the infrared spectrum of the background Io(ν), which is usually measured empirically. This variable quantifies the infrared source that is being passed through the sample of interest. When obtained empirically, the infrared spectrum of the background may also account for detector responsivity and atmospheric constituents in the infrared path that occur both with and without the sample. When making infrared measurements, the background spectrum must be updated frequently to correct for environmental and instrument changes over time. The background spectrum is usually obtained in a clean environment before a sample of interest is introduced to the system. To increase signal-to-noise, the background spectrum may be averaged over an extended period of time.
When infrared spectroscopy is used in a continuously monitoring point sensor, the sample interrogated by the point sensor may be contaminated at any time, and contamination could be due to threat chemicals or interferent chemicals. Threat chemicals are defined as those chemicals that require a user response, while interferent chemicals are all other chemicals. If historical spectra are to be used as a chemical background, the process that chooses these historical spectra must be automated, and the algorithm must somehow determine whether a candidate background spectrum is contaminated. Furthermore, a contaminant may be present over an extended period of time making it impossible to use an empirical spectrum that reflects the current state of the sensor.
An alternative approach is to periodically inject a clean air sample into the continuous monitor. However, clean air injection reduces the duty cycle of the system and requires accurate modeling of atmospheric constituents to accomplish background removal.
The prior art in another area, hyperspectral imaging, suggests an approach for modeling the spectral background. Hyperspectral imaging involves taking an optical image of a location of interest, where each pixel in the image contains a spectrum. When performed in the infrared, chemicals in the scene can be identified by extraction of chemical signatures from the spectra and comparison to library infrared spectra. The background problem for hyperspectral imaging is even more complicated than the simple infrared spectroscopy case. Each pixel in the image, which can contain sky, buildings, or geological features has unique spectral characteristics. A useful technique in hyperspectral imaging, referred to as the matched filter, is to treat the variation in the backgrounds as system noise and model the backgrounds using a mean spectrum and a covariance matrix. The mean vector and the covariance matrix are computed by using all of the spatial elements of the hyperspectral image. Standard least squares techniques can then be used to identify and quantify the chemical signatures present in the hyperspectral image.
Existing infrared spectroscopy systems and methods disadvantageously process threat spectra and interferent spectra identically and different than background spectra. These systems and methods have disadvantageously used static non-decaying backgrounds for threat detection. In hyperspectral imaging, the covariance and mean of the background model has been processed in the linear space, which would be disadvantageous for the continuous monitoring case because of the computational burden. The prior continuous monitoring systems and methods have insufficiently low false alarm rate and insufficiently high sensitivities for many applications. These and other disadvantages are solved or reduced by the present invention.